Novel Solutions to the Three-Anchor ToA-Based Three-Dimensional Positioning Problem

At least four non-coplanar anchor nodes (ANs) are required for the time-of-arrival (ToA)-based three-dimensional (3D) positioning to enable unique position estimation. Direct method (DM) and particle filter (PF) algorithms were developed to address the three-anchor ToA-based 3D positioning problem. The proposed DM reduces this problem to the solution of a quadratic equation, exploiting the knowledge about the workspace, to first estimate the x- or z-coordinate, and then the remaining two coordinates. The implemented PF uses 1000 particles to represent the posterior probability density function (PDF) of the AN’s 3D position. The prediction step generates new particles by a resampling procedure. The ToA measurements determine the importance of these particles to enable updating the posterior PDF and estimating the 3D position of the AN. Simulation results corroborate the viability of the developed DM and PF algorithms, in terms of accuracy and computational cost, in the pursuit and circumnavigation scenarios, and even with a horizontally coplanar arrangement of the three ANs. Therefore, it is possible to enable applications requiring real-time positioning, such as unmanned aerial vehicle (UAV) autonomous docking and circling a stationary (or moving) position, without the need for an excessive number of ANs.

Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients

Computed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.

Mediation of Coping Strategies among Patients with Advanced Cancer

The purpose of this study was to test the mediating effects of coping on relationships of psychological distress and stress with anxiety, depression, and quality of life. A cross-sectional and correlational research study was used to recruit a sample of 440 patients with advanced cancer in Indonesia. A bootstrap resampling procedure was used to test the significance of the total and specific indirect effects of coping. Data analysis showed that problem-focused coping (PFC) mediated relationships of psychological distress and stress on depression, anxiety and functional well-being. PFC also mediated the relationship between stress and social well-being. Emotional-focused coping (EFC) mediated the relationship of stress with physical and emotional well-being. EFC also mediated the relationships between psychological distress and physical well-being. Thus, proper assessments and interventions should be tailored and implemented for patients in order to facilitate their use of coping strategies when needed in stressful situations.

A drift homotopy implicit particle filter method for nonlinear filtering problems

<p style='text-indent:20px;'>In this paper, we develop a drift homotopy implicit particle filter method. The methodology of our approach is to adopt the concept of drift homotopy in the resampling procedure of the particle filter method for solving the nonlinear filtering problem, and we introduce an implicit particle filter method to improve the efficiency of the drift homotopy resampling procedure. Numerical experiments are carried out to demonstrate the effectiveness and efficiency of our drift homotopy implicit particle filter.</p>

The Activation of Prothrombin Seems to Play an Earlier Role than the Complement System in the Progression of Colorectal Cancer: A Mass Spectrometry Evaluation

Colorectal cancer (CRC) is the second cause of death in men and the third in women. This work deals with the study of the low molecular weight protein fraction of sera from patients who underwent surgery for CRC and who were followed for several years thereafter. MALDI-TOF MS was used to identify serum peptidome profiles of healthy controls, non-metastatic CRC patients and metastatic CRC patients. A multiple regression model was applied to signals preliminarily selected by SAM analysis to take into account the age and gender differences between the groups. We found that, while a signal m/z 2021.08, corresponding to the C3f fragment of the complement system, appears significantly increased only in serum from metastatic CRC patients, a m/z 1561.72 signal, identified as a prothrombin fragment, has a significantly increased abundance in serum from non-metastatic patients as well. The findings were also validated by a bootstrap resampling procedure. The present results provide the basis for further studies on large cohorts of patients in order to confirm C3f and prothrombin as potential serum biomarkers. Thus, new and non-invasive tests might be developed to improve the classification of colorectal cancer.

Models for Expected Returns with Statistical Factors

In this paper, we propose multifactor models for the pan-European Equity Market using a block-bootstrap method and compare the results with those of traditional inferential techniques. The new factors are built from statistical measurements on stock prices—in particular, coefficient of variation, skewness, and kurtosis. Data come from Reuters, correspond to nearly 2000 EU companies, and span from January 2008 to February 2018. Regarding methodology, we propose a non-parametric resampling procedure that accounts for time dependency in order to test the validity of the model and the significance of the parameters involved. We compare our bootstrap-based inferential results with classical proposals (based on F-statistics). Methods under assessment are time-series regression, cross-sectional regression, and the Fama–MacBeth procedure. The main findings indicate that the two factors that better improve the Capital Asset Pricing Model with regard to the adjusted R2 in the time-series regressions are the skewness and the coefficient of variation. For this reason, a model including those two factors together with the market is thoroughly studied. We also observe that our block-bootstrap methodology seems to be more conservative with the null of the GRS test than classical procedures.

Tax professionals’ role and ethics during a period of economic crisis in a small European country

Research Question: This paper analyses the role and ethics of tax professionals in the collection of public revenue from business activity during a period of economic crisis in Greece. Motivation: We attempt to analyse the respective influences of a corporate environment and personal beliefs on tax professionals’ ethics as well as the consequences of economic crisis. Idea: The paper employs a modified experimental questionnaire from Bobek & Radtke (2007) for the US. This questionnaire is adapted for Greece during a period of economic crisis. Data: Addressees of the questionnaire were tax professionals of two categories: certified public accountants and accountant/tax consultants, both of which are responsible for determining the amount of taxes owed to the Greek Independent Public Revenue Authority (IPRA). Tools: Results were reached by submitting the results of a questionnaire to a multiple-correspondence analysis based on the Burt matrix. As the size of the sample is rather small, in order to avoid any bias the study employs a resampling procedure based on the k fold validation method. Findings: The results of the survey showed that the main causes of ethical dilemmas are problems with clients such as pressure from clients, client-retention concerns and misunderstandings with clients. The major factor contributing to the resolution of an ethical dilemma is the experience of the tax professional. A significant percentage of respondents believed that their level of morality had increased during the period of economic crisis, making them more compliant with relevant tax rules. Contribution: This study contributes to business ethics and helps reinforce them, thereby contributing to the increase in the public revenues, which can help a economy to emerge faster from an economic crisis. Also, we recommend the targeting on in-house ethics training and explicitly including rewards and sanctions regarding ethical behavior in performance evaluation systems in tax professionals’ firms.

Avoiding pitfalls: Bayes factors can be a reliable tool for post hoc data selection in implicit learning

Research on implicit processes has revealed problems with awareness categorizations based on non-significant results. Moreover, post-hoc categorizations result in regression to the mean (RTM), by which aware participants are wrongly categorized as unaware. Using Bayes factors to obtain sensitive evidence for participants’ lack of knowledge may deal with non-significance being non-evidential but also may prevent regression-to-the-mean effects. Here we examine the reliability of a novel Bayesian awareness categorization procedure.Participants completed a reward learning task followed by a Flanker task measuring attention towards conditioned stimuli. They were categorized as B_Aware and B_Unaware of stimulus-outcome contingencies, and those with insensitive Bayes factors were deemed B_Insensitive. We found that performance for B_Unaware participants was below chance level using unbiased tests. This was further confirmed using a resampling procedure with multiple iterations, contrary to the prediction of RTM effects. Conversely, when categorizing participants using t-tests, t_Unaware participants showed RTM effects. We also propose a group boundary optimization procedure to determine the threshold at which regression to the mean is observed. Using Bayes factors instead of t-tests as a post-hoc categorization tool allows evaluating evidence of unawareness, which in turn helps avoid RTM. The reliability of the Bayesian awareness categorization procedure strengthens previous evidence for implicit reward conditioning. The toolbox used for the categorization procedure is detailed and made available. Post-hoc group selection can provide evidence for implicit processes; the relevance of RTM needs to be considered for each study and cannot simply be assumed to be a problem.

Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes

In this article, the four-anchor time difference of arrival (TDoA)-based three-dimensional (3D) positioning by particle filtering is addressed. The implemented particle filter uses 1000 particles to represent the probability density function (pdf) of interest, i.e., the posterior pdf of the target node’s state (position). A resampling procedure is used to generate particles in the prediction step, and TDoA measurements are used to determine the importance, i.e., weight, of each particle to enable updating the posterior pdf and estimating the position of the target node. The simulation results show the feasibility of this approach and the possibility to employ it in indoor positioning applications under the assumed working conditions using, e.g., the ultra-wideband (UWB) wireless technology. Therefore, it is possible to enable unmanned air vehicle (UAV) positioning applications, e.g., inventory management in large warehouses, without the need for an excessive number of anchor nodes.

TENFOLD BOOTSTRAP PROCEDURE FOR SUPPORT VECTOR MACHINES

Cross validation is often used to split input data into training and test set in Support vector machines. The two most commonly used cross validation versions are the tenfold and leave-one-out cross validation. Another commonly used resampling method is the random test/train split. The advantage of these methods is that they avoid overfitting in the model and perform model selection. They, however, can increase the computational time for fitting Support vector machines with the increase of the size of the dataset. In this research, we propose an alternative for fitting SVM, which we call the tenfold bootstrap for Support vector machines. This resampling procedure can significantly reduce execution time despite the big number of observations, while preserving model’s accuracy. With this finding, we propose a solution to the problem of slow execution time when fitting support vector machines on big datasets.